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Please create the outline(Proposal) and a research paper In APA format:

OUTLINE Must Include:

• Title page with title, name date, class, professor, and university info
• Top level headers which outline what you will be talking about in your paper (major topics
• Second level headers which outline specific detail headers for each of your top level headers.
• A bibliography list with at least 5 sources.
• Research PaperResearch 

Modern Day Attacks Against Wireless Networks
Prepare a 6-8 page paper in Microsoft Word (counts as 12% of the final grade) AMU approved APA format (see writing expectations in the Policies section) (300-350 words per page).

At a minimum include the following:
References (minimum of 10)

You may use resources from the APUS Online Library, any library, government library, or any peer-reviewed reference (Wikipedia and any other publicly-reviewed source is not accepted). The paper must by at least 6-8 pages double-spaced, 1" margin all around, black, 12 point font (Times New Roman) with correct citations of all utilized references/sources, (pictures, graphics, etc are extra - allowed but extra for the minimum page count). The title page and references are also required but don't count in the minimum page count.

 

I check for Plagiarism

I have examples of what I am looking for attached.

My Research Paper Topic is attached also.

A big TIP is included for a high quality paper

 

 

BIG DATA AND ANALYTICS Computer Technology Trend: Big data and Analytics
Paul Cailleteau
AMU/APU
Instructor: Dustin Marema BIG DATA AND ANALYTICS
Big Data and Analytics
Big data is the adamant trend in the field of computer science because with the coming of
new computer science engineering, device, and versatile means of communication such as the
social network platforms. Therefore, to process and store this voluminous data is impossible if
we do not use the current latest computer science technology like Big Data. Big data provides the
mechanism to store high volume structures as well as unstructured data in an effective and
efficient format so that later on it can be processed to make data useful for the decision-making.
Data is increasing with very rapid speed, and if this data is managed properly then very soon we
might not be able to store and process the large data. Due to the various nature and purpose of
data, it is becoming the unstructured data and it cannot be stored in traditional level data format.
So we need some storage mechanism which can store the mixed or unstructured data and later
some provision should be provided so that it can be processed. Some examples of Big data are
power grid data, stock exchange data, black box data, social media data, search engine data,
telecom operator transaction data and list goes on. Big data includes data types like structured
data, semi-structures data, unstructured data like the word, pdf, audio, video, etc.
Analytics is the mechanism of processing data, which works on the immense data storage
as well as extracting the functional info out of it to help agencies look for the information that
has been deduced from the platform of analyzing and utilizing it as the basis for making its
decisions. Analytics can be used to plot the data in various forms such as a graph, chart and will
trend over time to enable the executive of the firm to easily and quickly look at the visually
displayed statistics and understand it more easily. Analytics produce the consumable and
functional data out of the Big data. Therefore, without the proper analytics of the data processing
platform, Big data is worthless because the owner of the data is not going to achieve anything. BIG DATA AND ANALYTICS
Big data and analytics are good for any organizations if they use data efficiently.
Technology involved in Big Data and Analytics
Big data is just the name to handle huge volumes of mixed and unstructured data, but in the
background lots of techniques are being utilized to store huge volume of data. Below is a list of
some of the top technologies:
Column-oriented database: Big data uses the column-oriented database instead of the
traditional row-oriented database. This kind of database provides a very fast result for the search
queries where data volume is very huge. Therefore, this kind of database technology is right from
the query performance perspective.
No-SQL: To implement big data No-SQL is being used. No-SQL is used to work on data, which
is not relational or arranged data. No-SQL is aimed to provide the east access for the vast
unstructured data and No-SQL data can be based on the key value pair.
Hadoop: Hadoop is an open root platform used to handle the big data and now a day it is very
popular among the organizations, because it has a big community to provide and seek the help
during the deployment or development. Hadoop basically stores data, and HDFS (Hadoop
distributed file system) file format and tons of tools and technology have been developed to read
and write the data in the HDFS file without going into the lower level of Hadoop
implementation. Hadoop is basically a popular implementation of the Map-Reduce programming
paradigm, which is the base of bi data processing, and storing.
Map Reduce: It is a programming method which allows a huge job execution scalability against
the thousands of computers and these computers can be commodity computing. A number of
computing devices can be increased if the job is larger than the current computing capacity. So as
per load is being increased, we can add the computer node in the cluster. BIG DATA AND ANALYTICS
Map-Reduce programming contains two types of task:
Map task: In this, an input data will be converted into the multiple databases on a different
computer, so that each computer can work on its database respectively. Therefore, an extensive
database will be fragmented into the small databases and will be stored into a different computer
so fast, processing can be done on that database.
Reduce task: Reduce function used to combine the results from all the computing nodes so that
final result can be achieved.
Future trends in the Big data and Analytics
Now in the IT industry, every organization that is involved with the data generation of
data processing, is moving towards the Big Data and Analytics so that maximum analysis can get
it from the raw data. So if big data is increasing at this rate then in future, it going to create some
impact and generate some trend as well.
Below is a list of some future direction of the Big data:
Data volume will keep on growing: So as of now, a lot of companies are collecting all the
possible data about their businesses, because in the future this data volume is going to increase
tremendously and the data generator will create data points so that more data can be collected
and a discrete analysis can be done on that specific data. Nowadays, lots of technology like IoT
(Internet of things), connected cars and connected devices are coming on the market. So all these
things are going to create a huge volume of data.
More nouveau tools for analyzing big data will be introduced into the market: As Big data is
becoming the industry standard, automation will be a big future trend in the field of the big data.
As normal users, people are habitual of the using the relational database and they don’t want to
go into the typicality of the big data, so organizations will take care of this by providing the BIG DATA AND ANALYTICS
efficient automation of the big data platform and the user can quickly deploy and configure the
big data platform and use it for their business benefit like streaming live data, making decision
on live data and taking the corrective action to correct it if something went wrong.
Automation of the Big data related Programming and abstraction of the difficulty of Big
data from user: Right now lots of manual works are being done while installing and configuring
the big data platform, which is prone to human error. So in the coming years, it needs to be
automated, so that user don’t need to go into the core level of the Big Data platform. They only
need to know high level. The user just needs to configure the data input and output stream, the
rest of the analysis and analytics parts will be taken care of by the automated tools where the user
only needs to provide what all they want to monitor and the tool will show internally. It will do
all the data processing and analysis part. So far, it will be easier for the user to concentrate on the
result part, not in the configuration and troubleshooting part.
Real-time decision-making: Big data analytics are going to provide the real-time analytics to
the organization, this is very effective. Big data concept is based on the cluster computing,
whether the speed of the computing and data processing can be increased by just adding the
additional nodes in the cluster. Therefore, the end processing speed will be very high so any
result or output can be achieved in real time and the concerned person can react on the output
and take corrective measure if required to improve any result out of the received data.
A large number of jobs in the field data scientist: This paradigm is going to create a lot of jobs
for the data scientist. Hopefully, the industry will be lacking the data scientist skill set to fulfill
the need in the upcoming future. The industry leaders have to take this into account and prepare
the skillset for the future, so that the growing need of the data scientist can be fulfilled.
Autonomous devices will increase: In coming future autonomous devices are going to increase. BIG DATA AND ANALYTICS
For example, Robots, autonomous vehicle, IoT devices, etc.
Companies involved in the area of Big data and Analytics
Currently, a majority of huge firms are involved with the big data where some companies
work as a Vendor Company, while some other works like a Consumer Company. So here the
consumer can be any business and to list some enterprises that are working in the vendor
company and enhancing the concept of big data for the natural use of others:
Cloudera: This company is a binary distributor of Hadoop platform to process and store the big
data.
Hortonworks: This Company is also the Binary distributor of the Hadoop platform and it is an
open source, so anyone can install the Hadoop and configure it for the big data processing.
HP: HP is providing the hardware and software for the storage and processing of big data.
Intel: Intel is also proving the big data hardware for easy, fast storage and retrieval of the huge
data and fast processing speed to produce real-time analytics.
Oracle: Oracle provides the data appliance for the high performance and secure platform for
running high workload and Hadoop and No-SQL based system.
Apache: Apache is the main pioneer in the foundation of Apache Hadoop platform. Apache
formulates open source projects to enhance the reliable distributed and scalable computing.
Apache provides the basis of a library for processing the high volume data in distributed manner
so that a fast result can be achieved. On top of the Hadoop Apache, it also offers lots of
automated application so that configuration and deployment become easy and more rapid. Some
examples of the application developed by Apache are- Tez, Zookeeper, Spark, Pig, Hive, HBase,
Chukwa, Cassandra, Avro, and Ambari. BIG DATA AND ANALYTICS
Regulatory issues surrounding the Big Data and Analytics
As with the increased volume of data and much of the personalized data about the
consumer or devices, it becomes imperative to think about the privacy of the data. Companies
involved in the data gathering should not collect and process the personalized data so that user’s
privacy can be kept private. Other Legal can be like transmitting data across the globe so that
another country or group cannot use that data for their personal interest. Big data analytics
should only be used to collect and process the data for the business intelligence and predictive
analysis or trend so that future business policy can be revived to meet the customer expectation.
Global implications of the Big Data Analytics
Big data have revolutionized the way of doing business. Big data technologies are being
used for the predictive analysis of the extensive data and helping the organization to predict the
future trend or future of the firm. Predictive analysis is based on the previous data trend, and it is
helping to group a lot of active decision making and reducing the expenditure to achieve the high
business output. Big data has given the birth too new dimension, which is totally based on the
data analysis and business intelligence. It is creating huge job vacancy for the data scientist and
creating a positive impact in the industry. Lots of startups are coming into the picture and
investors are investing on this to create the new paradigm of doing business across the globe. BIG DATA AND ANALYTICS
References
Emerging technologies for Big Data. Retrieved from http://www.techrepublic.com/blog/big-dataanalytics/10-emerging-technologies-for-big-data/
Predictions About The Future Of Big Data Retrieved from
http://www.forbes.com/sites/bernardmarr/2016/03/15/17-predictions-about-the-future-of-bigdata-everyone-should-read/#60d03058157c
Big Data Vendors. Retrieved from http://www.bigdatavendors.com/top.php
Big Data Appliance. Retrieved from http://www.oracle.com/technetwork/database/bigdataappliance/overview/index.html
Welcome to Apache Hadoop. Retrieved from http://hadoop.apache.org/
Big data: managing the legal and regulatory risks | Information Age. Retrieved from
http://www.information-age.com/it-management/risk-and-compliance/123458663/big-datamanaging-legal-and-regulatory-risks
The Impacts of Big Data. Retrieved from
http://www.forbes.com/sites/peterpham/2015/08/28/the-impacts-of-big-data-that-you-may-nothave-heard-of/#8bc768fc957d
About Map Reduce. Retrieved from https://www01.ibm.com/software/data/infosphere/hadoop/mapreduce/
Big data and Predictive Analytics. Retrieved from http://www.gartner.com/it-glossary/predictiveanalytics/

Research Paper Outline
ISSC323
Paul Cailleteau
Dustin Marema
Big Data and Analytics Introduction
This research paper is based on the latest trending technology of computer science, that being
Big Data and Analytics. This paper will demonstrate how this technology is playing the vital role
in the field of computer science and how it is differentiating with the traditional approach of data
processing and extracting information from that. Analytics is the data processing mechanism
which processes the huge data storage and extracts the useful information out of that so that any
organization can look on the information derived from the analytics platform and take it as the
input for the decision making. Analytics can plot the data in the form of a chart, graph and trend
over time so that management can easily look at the visual information and easily interpret it.
Topics demonstrated in the Paper
In this paper I will explain various aspects of the topic big data and analytics which are listed
down as belowTechnology involved in Big Data and Analytics Column-oriented database No-SQL Hadoop Map Reduce Map task/Reduce Task Future trends in the Big data and analytics Data volume will keep on growing More tool for the big data analysis will come into the market Automation of the Big data Real time decision making Large number of job in the field data scientist Autonomous devices will increase Companies involved in the area of Big data and Analytics Cloudera Hortonworks HP/Intel Oracle Apache Regulatory issues surrounding the Big Data and Analytics Privacy Global implications of the Big Data Analytics Finding of the Research Paper
The point of this research paper is to understand this technology in depth and how it works and
what all tools and applications are available in the market, so that huge data is being processed
by the various organizations for their business intelligence and positive decision making. We also
tried to get rid of the regulatory complication regarding the storing and processing of huge data
and how user data privacy can be maintained while giving deep insight about the data for the
predictive decision taking. We also tried to dig out the circumstances of the big data and
analytics in the global market. How organizations are getting benefitted with the use of the data
analytics in the global market for the targeted decision making.

Paul Cailleteau
ISSC343 Wireless Networks
My Topic for the Research Paper:
Modern Day Attacks Against Wireless Networks

Attachments:

• 1493442675-Reasearch Paper Topic.docx
• 1493442676-ComputerTechnologyTrend_BigDataandAnalytics_ 1.docx
• 1493442676-Project Proposal for the big data and analytics.docx